Pretraining on Dynamic Graph Neural Networks

Last update: Dec 17, 2022

Related tags

Deep Learning PT-DGNN

Overview

Pretraining on Dynamic Graph Neural Networks

Our article is PT-DGNN and the code is modified based on GPT-GNN

Requirements

python 3.6
Ubuntu 18.04.5 LTS
torch 1.4.0+cu100
more packages are in requirements.txt

Runing example

pip install -r requirements.txt
python pretrain.py --args values(args and values are detailed instructions in the code)
python finetune.py --args values

Owner

GitHub Repository

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Pretraining on Dynamic Graph Neural Networks

Related tags

Overview

Pretraining on Dynamic Graph Neural Networks

Requirements

Runing example

Owner

An open-source online reverse dictionary.

A 3D sparse LBM solver implemented using Taichi

Deep learned, hardware-accelerated 3D object pose estimation

A sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes

Repo for EchoVPR: Echo State Networks for Visual Place Recognition

This is a library for training and applying sparse fine-tunings with torch and transformers.

Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Simultaneous NMT/MMT framework in PyTorch

Train CNNs for the fruits360 data set in NTOU CS「Machine Vision」class.

Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

The Fundamental Clustering Problems Suite (FCPS) summaries 54 state-of-the-art clustering algorithms, common cluster challenges and estimations of the number of clusters as well as the testing for cluster tendency.

Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty

PyGCL: Graph Contrastive Learning Library for PyTorch

Transferable Unrestricted Attacks, which won 1st place in CVPR’21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet.

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Official repo for our 3DV 2021 paper "Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements".

discovering subdomains, hidden paths, extracting unique links

[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior

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